From Gradient Analysis to Norm Control: Rethinking Triplet Loss for Gait Recognition

Gait recognition has attracted increasing attention in both academia and industry as a non-intrusive human recognition technology from a distance without requiring cooperation. Triplet loss, which enforces relative distance constraints, is a fundamental component in gait recognition. Recently, several gait-specific triplet losses have been introduced to gait recognition. However, they only focus on sample selection and weighting to enhance constraints without exploring the gradient properties of Cosine/Euclidean metrics, which fundamentally influence the model training efficiency and feature discriminability. In this paper, we theoretically analyze triplet loss gradients combined with weight decay and identify inherent limitations due to inadequate norm-control: Cosine metric triplet loss (Lcos) exhibits excessive gradients resulting from small feature norms, while Euclidean metric triplet loss (Leuc) suffers from a small margin-to-norm ratio due to large feature norms. To address these issues, we propose two norm-control approaches to constrain the feature norm in a stable range: 1) Norm-Variance-Regularized Collaboration. 2) Norm-Based Regularization. Extensive experiments show that our methods outperform state-of-the-art results under both Cosine and Euclidean evaluation metrics on three in-the-wild datasets: Gait3D, GREW, and BUAA-Duke-Gait. The code will be available at https://github.com/bgdpgz/TL-Gait.